Estimation of High-Frequency Mass Variations from Satellite Data in near Real-Time: Implementation of a Technology Demonstrator for near Real-Time GRACE/GRACE-FO Gravity Field Solutions

Research output: ThesisDoctoral Thesis

Abstract

Earth's time variable gravity field provides invaluable insights into the changing nature of our planet. As it is a proxy to mass variations on Earth's surface, it reflects geophysical processes like continental hydrology, changes in the cryosphere or mass flux in the ocean. Dedicated satellite missions such as the NASA/DLR operated Gravity Recovery and Climate Experiment (GRACE), and its successor GRACE Follow-On (GRACE-FO) continuously monitor these temporal variations of the gravitational attraction with global coverage. Both missions provide monthly snapshots of Earth's gravity field with a latency of about two months. While these data sets have fundamentally improved the knowledge of the temporal evolution of the geophysical interactions which compose the global climate system, there are a variety of processes happening on sub-monthly time scales. For example, short-lived events such as floods, which occur on the time frame of hours to weeks, require low latency monitoring of high-frequency mass variations in order to be properly resolved.

This thesis provides the theoretical foundation, implementation, and a review of an operational test run of a near real-time (NRT) processing scheme for spaceborne gravity observations. Building on the already well established Kalman filter approach for GRACE/GRACE-FO data, a robust, fully autonomous tech demonstrator for daily gravity field solutions with a latency of one day based on GRACE quicklook (Q/L) data was implemented. Even though the operational test run of the NRT processing scheme coincided with the last months of the GRACE mission, where deteriorating health of the on-board instrumentation resulted in a challenging environment, high-quality gravity field solutions were obtained. This was confirmed by a reanalysis of the observation data, where post-processing techniques could be applied.Due to the unique nature of gravity observations which, in contrast to other remote sensing techniques, provide information about the whole water column including ground water, this complementary NRT data set has the potential to contribute to and improve flood forecasting in the future.
Original languageEnglish
QualificationDoctor of Technology
Awarding Institution
  • Graz University of Technology (90000)
Supervisors/Advisors
  • Mayer-Gürr, Torsten, Supervisor
Award date24 Jan 2020
Publisher
Electronic ISBNs978-3-85125-771-7
DOIs
Publication statusPublished - 2020

Keywords

  • GRACE
  • near real-time
  • daily solutions

Fields of Expertise

  • Sustainable Systems

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